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  • Title: Can we predict anti-seizure medication response in focal epilepsy using machine learning?
    Author: Lee DA, Lee HJ, Park BS, Lee YJ, Park KM.
    Journal: Clin Neurol Neurosurg; 2021 Dec; 211():107037. PubMed ID: 34800813.
    Abstract:
    OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM response in focal epilepsy can be predicted using a machine learning approach. METHODS: In this retrospective study conducted at a tertiary hospital, we enrolled 160 patients with newly diagnosed focal epilepsy. All of them underwent DTI from January 2017 to July 2019, with a follow-up at least 12 months after the diagnosis of epilepsy based on regular evaluation of ASMs. We analyzed the patients' clinical characteristics, and the conventional DTI measurements and extracted the structural connectomic profiles from the DTI. We employed the support vector machine (SVM) algorithm, and a k-fold cross-validation was executed. RESULTS: The highest accuracy of classification was ensured based on the clinical factors. A SVM classifier based on the clinical factors revealed an accuracy of 87.5% and an area under curve (AUC) of 0.882. Another SVM classifier based on the conventional DTI measures demonstrated an accuracy of 62.5% and an AUC of 0.611. In addition, an SVM classifier based on the structural connectomic profiles revealed an accuracy of 68.7% and an AUC of 0.667. The AUC of the ROC curve generated from the clinical factors was significantly higher than the ROC curves based on the conventional DTI measures or structural connectomic profiles. CONCLUSION: Machine learning approach is useful in predicting the ASM response in focal epilepsy. The clinical factor is more important than the conventional DTI measures and structural connectomic profiles in predicting the ASM response in focal epilepsy.
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